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Abstract
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[ Livestream: Visit Generalization / Reinforcement Learning / Optimization ]

Thu 15 Apr 1:30 p.m. — 1:45 p.m. PDT

`density of states,'' the DoSE decision rule avoids direct comparison of model probabilities, and instead utilizes the`

probability of the model probability,'' or indeed the frequency of any reasonable statistic. The frequency is calculated using nonparametric density estimators (e.g., KDE and one-class SVM) which measure the typicality of various model statistics given the training data and from which we can flag test points with low typicality as anomalous. Unlike many other methods, DoSE requires neither labeled data nor OOD examples. DoSE is modular and can be trivially applied to any existing, trained model. We demonstrate DoSE's state-of-the-art performance against other unsupervised OOD detectors on previously established ``hard'' benchmarks.

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